What does AI Workflow Risk Radar deliver?

It maps workflow dependencies and failure chains, ranks risks using stated evidence and uncertainty, and produces control recommendations, test priorities, residual risks, and a conditional release recommendation.

Does it verify whether a workflow is safe or compliant?

No. It performs a design review from the information provided and labels assumptions and evidence gaps. It does not certify safety or compliance, inspect live behavior, or replace qualified reviewers.

What information should I provide for a useful review?

Share the workflow goal, triggers, actors, systems, data types, decisions, permissions, approvals, side effects, retries, timeouts, state handling, monitoring, and recovery steps. Mark unknowns explicitly and omit secrets or unnecessary personal data.

AI Workflow Risk Radar — Pre-Deployment Review

Example session with this skill installed

Review this proposed invoice approval workflow: an AI extracts invoice details, checks them against a purchase order, routes exceptions to a person, and sends approved invoices to the payment system. Identify risk chains involving mis-extraction, wrong vendor, duplicate payment, timeout/retry, race conditions, approval bypass, and poor recovery. Create controls, tests, residual risks, and a conditional release recommendation. Treat missing system details as unknown.

Read your context and instructions
Compiled the ai workflow risk

Release recommendation

Recommendation: DEFER production release; run a controlled design and validation review first. The workflow description is high level and does not identify the payment API contract, authorization model, duplicate controls, approval semantics, state store, or recovery behavior. This is not a finding that the design is unsafe; it is a statement that critical risks cannot yet be assessed from the supplied information.

Highest-priority risk chains

Risk chain	Potential outcome	Evidence gap	Priority control
OCR/AI misreads amount or vendor → deterministic match accepts wrong field → approval is treated as valid	Incorrect or misdirected payment	Extraction confidence, matching rules, tolerance, and labels unknown	Require field-level validation against authoritative source; route mismatches/low confidence to human review; log provenance
Payment request succeeds remotely → caller times out → retry submits again	Duplicate payment	Idempotency support and reconciliation behavior unknown	Verify exact endpoint behavior; stable transaction key or atomic dedupe; reconcile external state before retry
Two workers process the same approved invoice concurrently	Duplicate or conflicting writes	Locking and concurrency controls unknown	Enforce atomic state transition and uniqueness; test parallel replay
Approval is attached to stale or altered invoice data	Unauthorized amount/vendor change after approval	Approval binding and immutable versioning unknown	Bind approval to invoice version, vendor, amount, and currency; invalidate approval on change
Payment succeeds but local state update fails	Incorrect “pending/failed” status and unsafe replay	Crash-window handling unknown	Persist intent/result, reconcile on restart, and prevent blind resubmission
Prompt injection in invoice attachment influences agent behavior	Unsafe tool use or bypass	Document parsing and tool boundaries unknown	Treat attachments as untrusted data; enforce deterministic policy and scoped tools outside model judgment

Controls before a pilot

Define hard gates: allowed vendors, amount limits, currencies, required PO match, approval roles, prohibited actions, and manual review threshold.
Separate extraction from authorization. The model may propose fields; deterministic policy and accountable approvals decide whether a payment can proceed.
Use least privilege, tenant scoping, immutable audit events, correlation IDs, secrets handling, rate limits, and a kill switch.
Specify idempotency, timeout, retry, concurrency, reconciliation, and recovery behavior for each external side effect.
Minimize invoice data in prompts and logs; confirm retention, access, and approved processing boundaries.

Test priorities

Correct invoice, PO match, and valid approval create exactly one payment intent.
Wrong vendor, altered amount, currency mismatch, missing PO, duplicate invoice number, or expired approval blocks or routes to review.
Low-confidence or conflicting extraction never silently proceeds.
Timeout after remote success followed by retry reconciles to one payment.
Timeout before remote success supports a safe retry under the verified API contract.
Concurrent processing, duplicate event delivery, worker crash, and restart do not create multiple payments.
Malicious text embedded in an attachment cannot change policy or expand tool access.
Reconciliation detects provider/local state mismatch and routes it to an owned recovery queue.

Evidence needed for release

Exact payment endpoint documentation, idempotency and lookup guarantees, role/approval rules, version binding, data-retention terms, audit coverage, recovery runbook, and test results on representative sanitized invoices. Set measurable acceptance thresholds and rollback conditions before a limited canary. Residual risk and final approval belong to the designated workflow, finance, security, and privacy owners.

Connects securely to your tools. The creator never sees your data.

About this skill

Find workflow risks before production

AI Workflow Risk Radar reviews AI-powered and conventional automated workflows for failure paths that can harm customers, data, money, systems, or operations. It looks across the whole design—inputs, triggers, rules, agents, tools, APIs, permissions, people, queues, retries, state, side effects, and recovery—to find how individual failures can combine into business risk.

What it delivers

A workflow and dependency map covering assets, decisions, external effects, human approvals, and critical state.
Failure modes and risk chains across trigger/input, decision, integration, state, action, human, recovery, security, and observability layers.
Risk rankings tied to severity, likelihood evidence, exposure, detectability, reversibility, and uncertainty.
Preventive controls, detection signals, safe stop/fallback behavior, reconciliation, and recovery steps.
Risk-focused test priorities for normal, negative, edge, adversarial, retry, concurrency, timeout, restart, and partial-completion cases.
A residual-risk register and evidence-based release recommendation with explicit conditions.

Risk review method

Define scope, business outcomes, owners, users, data, systems, permissions, and unacceptable outcomes.
Map triggers, transitions, AI decisions, deterministic rules, integrations, side effects, approvals, and recovery paths.
Identify single failures and multi-step risk chains; mark assumptions and unknowns.
Prioritize safeguards and tests by materiality and evidence, keeping residual risk visible.
Recommend proceed, proceed with conditions, defer, or stop based on supplied requirements and evidence.

Important boundaries

This is a design and assurance review, not proof that a system is safe. It does not invent workflow behavior, incident history, controls, permissions, test results, integrations, or regulatory requirements. Current technical behavior must be verified against the exact system and version. Production approval remains with accountable owners and qualified security, privacy, legal, and operations reviewers.

How to use

Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.

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AI Workflow Risk Radar — Pre-Deployment Review